Self-Optimized Strategy for IO Accelerator Parametrization · Atos, the Atos logo, Atos Codex, Atos...
Transcript of Self-Optimized Strategy for IO Accelerator Parametrization · Atos, the Atos logo, Atos Codex, Atos...
28-06-2018
© Atos
Self-Optimized Strategy for IO Accelerator Parametrization
Lionel Vincent, Gaël Goret, Mamady Nabe, Trong Ton Pham
Bull - Atos Technologies
WOPSSS - 18
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We are THE European IT Leaderand a top 5 Digital services player worldwide
100,000
€ 12bn in 2016
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Infrastructure & Data Management
Big Data & Security700M€, 3500 people
Business & Platforms Solutions
WorldLine
This is our Mission within Atos
COMPLETEINTELLIGENTSYSTEMS
SECUREINSIGHT PLATFORMSSOFTWARE
HIGH-PERFORMANCE HARDWARE
We provide high–end technologies, insight platforms & intelligent systems to leverage & secure data in the digital age
Manage & Secure Data
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Big Data & HPC Business UnitEnd-to End-ofering to handle the most complex challenges
Data center
Data ManagementSupercomputers
Expertise & services
Software
HPCaaS/DLaaS
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Application Process
glibc
Fast IO Library
prefetch
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BDS - HPC R&D – Data ManagementProduct Overview
Accelerate
Fast IO Libraries&
Smart Burst Bufer IO Instrumentation
Collect
IO Pattern Analyzer
Sett
ing-
upApplicationV1.0
▶ Bull IO Instrumentation V1.1▶ Bull Fast IO Libraries V1.0▶ Bull Smart Burst Bufer (End 2018)▶ Bull IO Pattern Analyzer
– Compare jobs via GUI – Estimate accelerability– Automate FIOL activation– Automate
accelerators
parametrization
(In dev.)
5s timeframe
Bull IO Pattern Analyzer
Automate accelerators parametrization
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▶ Run a job with the minimal execution time
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Bull IO Pattern AnalyzerTargeted User Feature
SLURM
IOI
Job Submission
IO metrics feedback
How to fnd the optimal accelerators parametrization ?
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▶ Static parametrizationÞ mean performance for a wide range of applications : never optimal
▶ Dynamic parametrizationÞ online analysis of application behavior : too costly (high overhead)
▶ Self-optimized parametrizationÞ fnd the optimal static parametrization for the job : trade-of static/dynamic
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Parametrization approaches
Job1 Job2 Job3 Job4t
ParamAcc
Job1 Job2 Job3 Job4t
ParamAcc
Job1 Job2 Job3 Job4t
ParamAcc
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▶ Run a job with the minimal execution timeÞ Automatically fnd the optimal parametrization of the IO accelerators
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Bull IO Pattern Analyzer (IOPA)Targeted User Feature
SLURM
IOIIOPA
Job SubmissionJob submitted with the best acceleration confguration
IO metrics feedback
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Self-optimized parametrization method
IO metrics collecton IO metricsParametersPerf Value
Runs Database
Inference
Free Meta-data(To be optiized)
ProposedMeta-data
Cluster
All runs
Fixed Meta-data(User defned)
FamilyFilter
Sub-set of family related runs
Discriminator
Topology
Paraieters
Job
Meta-data
Clustering (Outlier
detecton)Relevant runs• Parameters• Perf Value
Threshold
IOIIO Instrumentation
IO Accelerators
IO Pattern Analyzer
Inference
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InferenceThe « equation »
Inference(of optmal Parameters)
Numericaloptmizaton
Modeling the objectvefuncton
▶ Theoretical ▶ Interpolation▶ Regression ✔
✘
✘
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▶ Find a model which estimates the relationship :Þ accelerators parameters vs performance
▶ Diferent methods studied– Bayesian Ridge Regression (BRR)– Kernel Ridge Regression (KRR)
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Inference - RegressionFrom principle to application
ƒ(param) = perfG
PR
KR
R
SV
R
BR
RRMSE
Methods Prediction time* Train time*
SVR 0,001 s 0,110 s
KRR 0,001 s 0,120 s
GPR 0,001 s 0,069 s
* Time measured to perform regression/prediction on 168 runs
– Support Vector Machines for Regression (SVR)– Gaussian Process Regression (GPR)
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Inference - Optimization Gradient-free optimization methods
Particle Swarm Optimization (PSO)▶ Use collective behavior to model the problem▶ They are described by a position and a velocity
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
▶ Find the optimal solutions of the IO accelerator parameters (min execution time)Þ Use heuristics to fnd « good » solutions in a reasonable time
▶ Gradient-free methodÞ Less sensitive to local minimum locking
Nelder-Mead (NM)▶ A simplex inspired
method▶ Based on four main
transformations
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# of initial points (runs)
Convergence speed
Mean
-Std
+Std
# of initial points (runs)
Convergence speed
Mean
-Std
+Std
IO metrics collecton IO metricsParametersPerf Value
Runs Database
Inference
Free Meta-data(To be optiized)Proposed
Meta-data
Cluster
All runs
Fixed Meta-data(User
defned)
FamilyFilter
Sub-set of family related runs+ Threshold
Discriminator
Topology
Paraieters
Job
Meta-data
Clustering (Outlier
detecton)Relevant runs• Parameters• Perf Value
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Inference Convergence validation : simulations
x
xx
x
xxnew runs
set of runs
Quadratic
StiblinskyRosenbrock
# of initial points (runs)
Convergence speed
Mean
-Std
+Std
To be validated on real jobs
Þon-going work
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▶ Chose the most relevant optimization algorithm
▶ Setup a parametrization strategy for initialization
▶ Implement optimization and regression features into our IOPA product16
Inference Perspectives
x
xx
x
x
CMA-ES
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